Overview

Brought to you by YData

Dataset statistics

Number of variables28
Number of observations186
Missing cells130
Missing cells (%)2.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory40.8 KiB
Average record size in memory224.7 B

Variable types

Numeric7
Categorical18
Text3

Alerts

Degree is highly overall correlated with SubjectHigh correlation
Favorite_Subject_12 is highly overall correlated with Marks_10High correlation
Gender is highly overall correlated with df_indexHigh correlation
Marks_10 is highly overall correlated with Favorite_Subject_12High correlation
Marks_12 is highly overall correlated with Percent_12High correlation
Percent_12 is highly overall correlated with Marks_12High correlation
Subject is highly overall correlated with Degree High correlation
df_index is highly overall correlated with GenderHigh correlation
Subject has 20 (10.8%) missing values Missing
Favorite_Subject_10 has 3 (1.6%) missing values Missing
Marks_10 has 11 (5.9%) missing values Missing
Favorite_Subject_12 has 12 (6.5%) missing values Missing
Marks_12 has 12 (6.5%) missing values Missing
co-curricular activity has 28 (15.1%) missing values Missing
Expected_salary has 13 (7.0%) missing values Missing
Spending has 30 (16.1%) missing values Missing
df_index is uniformly distributed Uniform
df_index has unique values Unique
CGPA has 24 (12.9%) zeros Zeros
Age has 10 (5.4%) zeros Zeros

Reproduction

Analysis started2025-09-06 07:51:36.059834
Analysis finished2025-09-06 07:51:45.787143
Duration9.73 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

df_index
Real number (ℝ)

High correlation  Uniform  Unique 

Distinct186
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean95.198925
Minimum0
Maximum191
Zeros1
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2025-09-06T07:51:45.917286image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9.25
Q147.25
median94.5
Q3142.75
95-th percentile181.75
Maximum191
Range191
Interquartile range (IQR)95.5

Descriptive statistics

Standard deviation55.800787
Coefficient of variation (CV)0.58614934
Kurtosis-1.200575
Mean95.198925
Median Absolute Deviation (MAD)48
Skewness0.01080067
Sum17707
Variance3113.7278
MonotonicityStrictly increasing
2025-09-06T07:51:46.071260image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
0.5%
1 1
 
0.5%
2 1
 
0.5%
3 1
 
0.5%
4 1
 
0.5%
5 1
 
0.5%
6 1
 
0.5%
7 1
 
0.5%
8 1
 
0.5%
9 1
 
0.5%
Other values (176) 176
94.6%
ValueCountFrequency (%)
0 1
0.5%
1 1
0.5%
2 1
0.5%
3 1
0.5%
4 1
0.5%
5 1
0.5%
6 1
0.5%
7 1
0.5%
8 1
0.5%
9 1
0.5%
ValueCountFrequency (%)
191 1
0.5%
190 1
0.5%
189 1
0.5%
188 1
0.5%
187 1
0.5%
186 1
0.5%
185 1
0.5%
184 1
0.5%
183 1
0.5%
182 1
0.5%

Age Group
Categorical

Distinct3
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
18 - 20
91 
20 - 22
86 
22 - 25
 
9

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters1302
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row18 - 20
2nd row20 - 22
3rd row18 - 20
4th row18 - 20
5th row20 - 22

Common Values

ValueCountFrequency (%)
18 - 20 91
48.9%
20 - 22 86
46.2%
22 - 25 9
 
4.8%

Length

2025-09-06T07:51:46.229793image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-06T07:51:46.315726image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
186
33.3%
20 177
31.7%
22 95
17.0%
18 91
16.3%
25 9
 
1.6%

Most occurring characters

ValueCountFrequency (%)
2 376
28.9%
372
28.6%
- 186
14.3%
0 177
13.6%
1 91
 
7.0%
8 91
 
7.0%
5 9
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1302
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 376
28.9%
372
28.6%
- 186
14.3%
0 177
13.6%
1 91
 
7.0%
8 91
 
7.0%
5 9
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1302
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 376
28.9%
372
28.6%
- 186
14.3%
0 177
13.6%
1 91
 
7.0%
8 91
 
7.0%
5 9
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1302
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 376
28.9%
372
28.6%
- 186
14.3%
0 177
13.6%
1 91
 
7.0%
8 91
 
7.0%
5 9
 
0.7%

Gender
Categorical

High correlation 

Distinct3
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
f
95 
m
90 
o
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters186
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st rowf
2nd rowf
3rd rowf
4th rowf
5th rowf

Common Values

ValueCountFrequency (%)
f 95
51.1%
m 90
48.4%
o 1
 
0.5%

Length

2025-09-06T07:51:46.410311image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-06T07:51:46.803015image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
f 95
51.1%
m 90
48.4%
o 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
f 95
51.1%
m 90
48.4%
o 1
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 186
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
f 95
51.1%
m 90
48.4%
o 1
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 186
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
f 95
51.1%
m 90
48.4%
o 1
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 186
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
f 95
51.1%
m 90
48.4%
o 1
 
0.5%
Distinct59
Distinct (%)31.7%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2025-09-06T07:51:47.057460image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length64
Median length52
Mean length27.698925
Min length6

Characters and Unicode

Total characters5152
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27 ?
Unique (%)14.5%

Sample

1st rowinstitute of engineering and management
2nd rowinstitute of engineering and management
3rd rowasutosh college
4th rowsister nivedita university
5th rowsister nivedita university
ValueCountFrequency (%)
university 80
 
12.2%
college 69
 
10.6%
of 38
 
5.8%
engineering 28
 
4.3%
sister 27
 
4.1%
nivedita 26
 
4.0%
and 26
 
4.0%
st 24
 
3.7%
management 22
 
3.4%
institute 21
 
3.2%
Other values (116) 293
44.8%
2025-09-06T07:51:47.494284image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 584
11.3%
i 507
 
9.8%
468
 
9.1%
n 451
 
8.8%
a 408
 
7.9%
t 381
 
7.4%
s 324
 
6.3%
r 273
 
5.3%
o 217
 
4.2%
l 207
 
4.0%
Other values (18) 1332
25.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5152
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 584
11.3%
i 507
 
9.8%
468
 
9.1%
n 451
 
8.8%
a 408
 
7.9%
t 381
 
7.4%
s 324
 
6.3%
r 273
 
5.3%
o 217
 
4.2%
l 207
 
4.0%
Other values (18) 1332
25.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5152
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 584
11.3%
i 507
 
9.8%
468
 
9.1%
n 451
 
8.8%
a 408
 
7.9%
t 381
 
7.4%
s 324
 
6.3%
r 273
 
5.3%
o 217
 
4.2%
l 207
 
4.0%
Other values (18) 1332
25.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5152
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 584
11.3%
i 507
 
9.8%
468
 
9.1%
n 451
 
8.8%
a 408
 
7.9%
t 381
 
7.4%
s 324
 
6.3%
r 273
 
5.3%
o 217
 
4.2%
l 207
 
4.0%
Other values (18) 1332
25.9%

Degree
Categorical

High correlation 

Distinct16
Distinct (%)8.6%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
b.sc / bs
59 
b tech.
40 
ba
27 
b com.
16 
bba
12 
Other values (11)
32 

Length

Max length9
Median length8
Mean length6.0107527
Min length2

Characters and Unicode

Total characters1118
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)1.6%

Sample

1st rowbba
2nd rowbba
3rd rowb.sc / bs
4th rowba
5th rowmsc / ms

Common Values

ValueCountFrequency (%)
b.sc / bs 59
31.7%
b tech. 40
21.5%
ba 27
14.5%
b com. 16
 
8.6%
bba 12
 
6.5%
llb 11
 
5.9%
ma 4
 
2.2%
msc / ms 4
 
2.2%
m com. 2
 
1.1%
bds 2
 
1.1%
Other values (6) 9
 
4.8%

Length

2025-09-06T07:51:47.626162image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
63
17.0%
b.sc 59
15.9%
bs 59
15.9%
b 56
15.1%
tech 40
10.8%
ba 27
7.3%
com 18
 
4.9%
bba 12
 
3.2%
llb 11
 
3.0%
ma 4
 
1.1%
Other values (10) 21
 
5.7%

Most occurring characters

ValueCountFrequency (%)
b 247
22.1%
184
16.5%
s 133
11.9%
c 123
11.0%
. 117
10.5%
/ 63
 
5.6%
a 48
 
4.3%
t 41
 
3.7%
e 41
 
3.7%
h 40
 
3.6%
Other values (6) 81
 
7.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1118
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
b 247
22.1%
184
16.5%
s 133
11.9%
c 123
11.0%
. 117
10.5%
/ 63
 
5.6%
a 48
 
4.3%
t 41
 
3.7%
e 41
 
3.7%
h 40
 
3.6%
Other values (6) 81
 
7.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1118
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
b 247
22.1%
184
16.5%
s 133
11.9%
c 123
11.0%
. 117
10.5%
/ 63
 
5.6%
a 48
 
4.3%
t 41
 
3.7%
e 41
 
3.7%
h 40
 
3.6%
Other values (6) 81
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1118
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
b 247
22.1%
184
16.5%
s 133
11.9%
c 123
11.0%
. 117
10.5%
/ 63
 
5.6%
a 48
 
4.3%
t 41
 
3.7%
e 41
 
3.7%
h 40
 
3.6%
Other values (6) 81
 
7.2%

Subject
Categorical

High correlation  Missing 

Distinct45
Distinct (%)27.1%
Missing20
Missing (%)10.8%
Memory size1.6 KiB
statistics
28 
english
22 
computer science engineering
15 
accountancy
10 
mathematics
 
8
Other values (40)
83 

Length

Max length63
Median length36
Mean length14.325301
Min length7

Characters and Unicode

Total characters2378
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique24 ?
Unique (%)14.5%

Sample

1st rowanalytics
2nd rowfinance
3rd rowstatistics
4th rowpolitical science
5th rowpsychology

Common Values

ValueCountFrequency (%)
statistics 28
15.1%
english 22
 
11.8%
computer science engineering 15
 
8.1%
accountancy 10
 
5.4%
mathematics 8
 
4.3%
finance 7
 
3.8%
microbiology 6
 
3.2%
life sciences 5
 
2.7%
psychology 5
 
2.7%
economics 4
 
2.2%
Other values (35) 56
30.1%
(Missing) 20
 
10.8%

Length

2025-09-06T07:51:47.753505image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
statistics 28
 
10.5%
science 27
 
10.2%
english 22
 
8.3%
computer 21
 
7.9%
engineering 20
 
7.5%
accountancy 10
 
3.8%
and 9
 
3.4%
mathematics 8
 
3.0%
finance 7
 
2.6%
microbiology 6
 
2.3%
Other values (54) 108
40.6%

Most occurring characters

ValueCountFrequency (%)
i 263
11.1%
e 252
10.6%
c 233
9.8%
n 230
9.7%
s 197
 
8.3%
t 195
 
8.2%
a 147
 
6.2%
o 138
 
5.8%
103
 
4.3%
g 103
 
4.3%
Other values (16) 517
21.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2378
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 263
11.1%
e 252
10.6%
c 233
9.8%
n 230
9.7%
s 197
 
8.3%
t 195
 
8.2%
a 147
 
6.2%
o 138
 
5.8%
103
 
4.3%
g 103
 
4.3%
Other values (16) 517
21.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2378
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 263
11.1%
e 252
10.6%
c 233
9.8%
n 230
9.7%
s 197
 
8.3%
t 195
 
8.2%
a 147
 
6.2%
o 138
 
5.8%
103
 
4.3%
g 103
 
4.3%
Other values (16) 517
21.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2378
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 263
11.1%
e 252
10.6%
c 233
9.8%
n 230
9.7%
s 197
 
8.3%
t 195
 
8.2%
a 147
 
6.2%
o 138
 
5.8%
103
 
4.3%
g 103
 
4.3%
Other values (16) 517
21.7%

Percent_10
Categorical

Distinct5
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
95.0
85 
85.0
67 
75.0
21 
65.0
 
8
55.0
 
5

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters744
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row85.0
2nd row85.0
3rd row85.0
4th row95.0
5th row65.0

Common Values

ValueCountFrequency (%)
95.0 85
45.7%
85.0 67
36.0%
75.0 21
 
11.3%
65.0 8
 
4.3%
55.0 5
 
2.7%

Length

2025-09-06T07:51:47.873338image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-06T07:51:47.959932image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
95.0 85
45.7%
85.0 67
36.0%
75.0 21
 
11.3%
65.0 8
 
4.3%
55.0 5
 
2.7%

Most occurring characters

ValueCountFrequency (%)
5 191
25.7%
. 186
25.0%
0 186
25.0%
9 85
11.4%
8 67
 
9.0%
7 21
 
2.8%
6 8
 
1.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 744
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5 191
25.7%
. 186
25.0%
0 186
25.0%
9 85
11.4%
8 67
 
9.0%
7 21
 
2.8%
6 8
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 744
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5 191
25.7%
. 186
25.0%
0 186
25.0%
9 85
11.4%
8 67
 
9.0%
7 21
 
2.8%
6 8
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 744
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5 191
25.7%
. 186
25.0%
0 186
25.0%
9 85
11.4%
8 67
 
9.0%
7 21
 
2.8%
6 8
 
1.1%

Favorite_Subject_10
Categorical

Missing 

Distinct34
Distinct (%)18.6%
Missing3
Missing (%)1.6%
Memory size1.6 KiB
mathematics
49 
english
30 
biology
17 
geography
12 
maths
12 
Other values (29)
63 

Length

Max length21
Median length20
Mean length8.5191257
Min length3

Characters and Unicode

Total characters1559
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17 ?
Unique (%)9.3%

Sample

1st rowbiology
2nd rowmathematics
3rd rowmathematics
4th rowsocialscience
5th rowmaths

Common Values

ValueCountFrequency (%)
mathematics 49
26.3%
english 30
16.1%
biology 17
 
9.1%
geography 12
 
6.5%
maths 12
 
6.5%
science 11
 
5.9%
history 7
 
3.8%
computer 4
 
2.2%
bengali 4
 
2.2%
physics 4
 
2.2%
Other values (24) 33
17.7%

Length

2025-09-06T07:51:48.095250image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
mathematics 50
27.3%
english 30
16.4%
biology 17
 
9.3%
geography 13
 
7.1%
maths 12
 
6.6%
science 12
 
6.6%
history 8
 
4.4%
physics 5
 
2.7%
computer 4
 
2.2%
bengali 4
 
2.2%
Other values (18) 28
15.3%

Most occurring characters

ValueCountFrequency (%)
i 156
10.0%
e 153
9.8%
s 145
9.3%
a 143
9.2%
t 141
9.0%
m 134
8.6%
h 125
8.0%
c 121
7.8%
o 82
 
5.3%
g 79
 
5.1%
Other values (12) 280
18.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1559
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 156
10.0%
e 153
9.8%
s 145
9.3%
a 143
9.2%
t 141
9.0%
m 134
8.6%
h 125
8.0%
c 121
7.8%
o 82
 
5.3%
g 79
 
5.1%
Other values (12) 280
18.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1559
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 156
10.0%
e 153
9.8%
s 145
9.3%
a 143
9.2%
t 141
9.0%
m 134
8.6%
h 125
8.0%
c 121
7.8%
o 82
 
5.3%
g 79
 
5.1%
Other values (12) 280
18.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1559
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 156
10.0%
e 153
9.8%
s 145
9.3%
a 143
9.2%
t 141
9.0%
m 134
8.6%
h 125
8.0%
c 121
7.8%
o 82
 
5.3%
g 79
 
5.1%
Other values (12) 280
18.0%

Marks_10
Real number (ℝ)

High correlation  Missing 

Distinct30
Distinct (%)17.1%
Missing11
Missing (%)5.9%
Infinite0
Infinite (%)0.0%
Mean90.88
Minimum3
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2025-09-06T07:51:48.214975image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile75.7
Q187
median94
Q397
95-th percentile100
Maximum100
Range97
Interquartile range (IQR)10

Descriptive statistics

Standard deviation10.57553
Coefficient of variation (CV)0.11636807
Kurtosis27.671196
Mean90.88
Median Absolute Deviation (MAD)4
Skewness-4.0548706
Sum15904
Variance111.84184
MonotonicityNot monotonic
2025-09-06T07:51:48.348498image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
97 18
 
9.7%
96 18
 
9.7%
98 14
 
7.5%
85 13
 
7.0%
95 12
 
6.5%
100 12
 
6.5%
99 11
 
5.9%
94 10
 
5.4%
90 8
 
4.3%
91 7
 
3.8%
Other values (20) 52
28.0%
(Missing) 11
 
5.9%
ValueCountFrequency (%)
3 1
0.5%
56 1
0.5%
57 1
0.5%
60 1
0.5%
68 1
0.5%
70 2
1.1%
75 2
1.1%
76 1
0.5%
78 1
0.5%
79 1
0.5%
ValueCountFrequency (%)
100 12
6.5%
99 11
5.9%
98 14
7.5%
97 18
9.7%
96 18
9.7%
95 12
6.5%
94 10
5.4%
93 4
 
2.2%
92 7
 
3.8%
91 7
 
3.8%

Percent_12
Categorical

High correlation 

Distinct5
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
85.0
79 
95.0
65 
75.0
34 
65.0
 
6
55.0
 
2

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters744
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row75.0
2nd row75.0
3rd row85.0
4th row85.0
5th row65.0

Common Values

ValueCountFrequency (%)
85.0 79
42.5%
95.0 65
34.9%
75.0 34
18.3%
65.0 6
 
3.2%
55.0 2
 
1.1%

Length

2025-09-06T07:51:48.463508image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-06T07:51:48.547564image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
85.0 79
42.5%
95.0 65
34.9%
75.0 34
18.3%
65.0 6
 
3.2%
55.0 2
 
1.1%

Most occurring characters

ValueCountFrequency (%)
5 188
25.3%
. 186
25.0%
0 186
25.0%
8 79
10.6%
9 65
 
8.7%
7 34
 
4.6%
6 6
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 744
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5 188
25.3%
. 186
25.0%
0 186
25.0%
8 79
10.6%
9 65
 
8.7%
7 34
 
4.6%
6 6
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 744
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5 188
25.3%
. 186
25.0%
0 186
25.0%
8 79
10.6%
9 65
 
8.7%
7 34
 
4.6%
6 6
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 744
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5 188
25.3%
. 186
25.0%
0 186
25.0%
8 79
10.6%
9 65
 
8.7%
7 34
 
4.6%
6 6
 
0.8%

Favorite_Subject_12
Categorical

High correlation  Missing 

Distinct34
Distinct (%)19.5%
Missing12
Missing (%)6.5%
Memory size1.6 KiB
mathematics
37 
english
29 
biology
22 
economics
10 
maths
Other values (29)
69 

Length

Max length20
Median length19
Mean length9.1321839
Min length3

Characters and Unicode

Total characters1589
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)6.9%

Sample

1st rowbiology
2nd roweconomics
3rd rowmathematics
4th rowpoliticalscience
5th rowaccounts

Common Values

ValueCountFrequency (%)
mathematics 37
19.9%
english 29
15.6%
biology 22
11.8%
economics 10
 
5.4%
maths 7
 
3.8%
chemistry 6
 
3.2%
psychology 6
 
3.2%
physics 6
 
3.2%
computerscience 6
 
3.2%
politicalscience 4
 
2.2%
Other values (24) 41
22.0%
(Missing) 12
 
6.5%

Length

2025-09-06T07:51:48.685157image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
mathematics 37
21.3%
english 29
16.7%
biology 22
12.6%
economics 10
 
5.7%
maths 7
 
4.0%
chemistry 6
 
3.4%
psychology 6
 
3.4%
physics 6
 
3.4%
computerscience 6
 
3.4%
politicalscience 4
 
2.3%
Other values (24) 41
23.6%

Most occurring characters

ValueCountFrequency (%)
i 158
9.9%
s 152
9.6%
c 146
9.2%
t 139
8.7%
e 136
8.6%
o 123
 
7.7%
m 118
 
7.4%
a 117
 
7.4%
h 99
 
6.2%
l 75
 
4.7%
Other values (9) 326
20.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1589
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 158
9.9%
s 152
9.6%
c 146
9.2%
t 139
8.7%
e 136
8.6%
o 123
 
7.7%
m 118
 
7.4%
a 117
 
7.4%
h 99
 
6.2%
l 75
 
4.7%
Other values (9) 326
20.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1589
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 158
9.9%
s 152
9.6%
c 146
9.2%
t 139
8.7%
e 136
8.6%
o 123
 
7.7%
m 118
 
7.4%
a 117
 
7.4%
h 99
 
6.2%
l 75
 
4.7%
Other values (9) 326
20.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1589
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 158
9.9%
s 152
9.6%
c 146
9.2%
t 139
8.7%
e 136
8.6%
o 123
 
7.7%
m 118
 
7.4%
a 117
 
7.4%
h 99
 
6.2%
l 75
 
4.7%
Other values (9) 326
20.5%

Marks_12
Real number (ℝ)

High correlation  Missing 

Distinct28
Distinct (%)16.1%
Missing12
Missing (%)6.5%
Infinite0
Infinite (%)0.0%
Mean90.057471
Minimum63
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2025-09-06T07:51:48.797190image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum63
5-th percentile73.3
Q185
median92
Q396.75
95-th percentile100
Maximum100
Range37
Interquartile range (IQR)11.75

Descriptive statistics

Standard deviation8.4022517
Coefficient of variation (CV)0.093298775
Kurtosis0.85401624
Mean90.057471
Median Absolute Deviation (MAD)6
Skewness-1.0469936
Sum15670
Variance70.597834
MonotonicityNot monotonic
2025-09-06T07:51:48.915260image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
100 17
 
9.1%
96 13
 
7.0%
85 12
 
6.5%
92 12
 
6.5%
95 11
 
5.9%
80 10
 
5.4%
99 10
 
5.4%
97 9
 
4.8%
90 9
 
4.8%
86 8
 
4.3%
Other values (18) 63
33.9%
(Missing) 12
 
6.5%
ValueCountFrequency (%)
63 1
 
0.5%
64 1
 
0.5%
65 2
 
1.1%
68 1
 
0.5%
70 1
 
0.5%
72 3
 
1.6%
74 1
 
0.5%
75 4
 
2.2%
77 1
 
0.5%
80 10
5.4%
ValueCountFrequency (%)
100 17
9.1%
99 10
5.4%
98 8
4.3%
97 9
4.8%
96 13
7.0%
95 11
5.9%
94 5
 
2.7%
93 6
 
3.2%
92 12
6.5%
91 4
 
2.2%

CGPA
Real number (ℝ)

Zeros 

Distinct9
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.3709677
Minimum0
Maximum10
Zeros24
Zeros (%)12.9%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2025-09-06T07:51:49.023373image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16
median7
Q38
95-th percentile9
Maximum10
Range10
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.696748
Coefficient of variation (CV)0.42328703
Kurtosis1.4072846
Mean6.3709677
Median Absolute Deviation (MAD)1
Skewness-1.5757825
Sum1185
Variance7.2724499
MonotonicityNot monotonic
2025-09-06T07:51:49.120395image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
7 59
31.7%
8 42
22.6%
6 30
16.1%
9 24
12.9%
0 24
12.9%
5 3
 
1.6%
10 2
 
1.1%
4 1
 
0.5%
1 1
 
0.5%
ValueCountFrequency (%)
0 24
12.9%
1 1
 
0.5%
4 1
 
0.5%
5 3
 
1.6%
6 30
16.1%
7 59
31.7%
8 42
22.6%
9 24
12.9%
10 2
 
1.1%
ValueCountFrequency (%)
10 2
 
1.1%
9 24
12.9%
8 42
22.6%
7 59
31.7%
6 30
16.1%
5 3
 
1.6%
4 1
 
0.5%
1 1
 
0.5%
0 24
12.9%
Distinct98
Distinct (%)62.0%
Missing28
Missing (%)15.1%
Memory size1.6 KiB
2025-09-06T07:51:49.355451image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length69
Median length46.5
Mean length13.512658
Min length2

Characters and Unicode

Total characters2135
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique86 ?
Unique (%)54.4%

Sample

1st rowno
2nd rowcontent writing
3rd rowdance
4th rownot right now
5th rowno
ValueCountFrequency (%)
no 31
 
9.3%
dance 22
 
6.6%
football 14
 
4.2%
yes 13
 
3.9%
singing 11
 
3.3%
11
 
3.3%
painting 9
 
2.7%
and 9
 
2.7%
dancing 8
 
2.4%
music 7
 
2.1%
Other values (131) 198
59.5%
2025-09-06T07:51:49.736426image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
234
 
11.0%
n 215
 
10.1%
a 191
 
8.9%
i 174
 
8.1%
e 140
 
6.6%
t 138
 
6.5%
o 130
 
6.1%
g 100
 
4.7%
s 98
 
4.6%
c 96
 
4.5%
Other values (26) 619
29.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2135
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
234
 
11.0%
n 215
 
10.1%
a 191
 
8.9%
i 174
 
8.1%
e 140
 
6.6%
t 138
 
6.5%
o 130
 
6.1%
g 100
 
4.7%
s 98
 
4.6%
c 96
 
4.5%
Other values (26) 619
29.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2135
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
234
 
11.0%
n 215
 
10.1%
a 191
 
8.9%
i 174
 
8.1%
e 140
 
6.6%
t 138
 
6.5%
o 130
 
6.1%
g 100
 
4.7%
s 98
 
4.6%
c 96
 
4.5%
Other values (26) 619
29.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2135
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
234
 
11.0%
n 215
 
10.1%
a 191
 
8.9%
i 174
 
8.1%
e 140
 
6.6%
t 138
 
6.5%
o 130
 
6.1%
g 100
 
4.7%
s 98
 
4.6%
c 96
 
4.5%
Other values (26) 619
29.0%
Distinct63
Distinct (%)33.9%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2025-09-06T07:51:50.020709image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length38
Median length27
Mean length14.247312
Min length2

Characters and Unicode

Total characters2650
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique43 ?
Unique (%)23.1%

Sample

1st rowdata analyst
2nd rowfinancial services
3rd rowchartered accountant
4th rowentrepreneurship
5th rowchartered accountant
ValueCountFrequency (%)
entrepreneurship 29
 
10.3%
researcher 20
 
7.1%
accountant 16
 
5.7%
chartered 14
 
5.0%
analyst 14
 
5.0%
data 13
 
4.6%
professor 13
 
4.6%
engineer 12
 
4.3%
software 11
 
3.9%
development 11
 
3.9%
Other values (77) 128
45.6%
2025-09-06T07:51:50.473221image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 405
15.3%
r 284
10.7%
n 228
 
8.6%
a 214
 
8.1%
t 201
 
7.6%
i 162
 
6.1%
s 160
 
6.0%
c 134
 
5.1%
o 125
 
4.7%
98
 
3.7%
Other values (15) 639
24.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2650
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 405
15.3%
r 284
10.7%
n 228
 
8.6%
a 214
 
8.1%
t 201
 
7.6%
i 162
 
6.1%
s 160
 
6.0%
c 134
 
5.1%
o 125
 
4.7%
98
 
3.7%
Other values (15) 639
24.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2650
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 405
15.3%
r 284
10.7%
n 228
 
8.6%
a 214
 
8.1%
t 201
 
7.6%
i 162
 
6.1%
s 160
 
6.0%
c 134
 
5.1%
o 125
 
4.7%
98
 
3.7%
Other values (15) 639
24.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2650
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 405
15.3%
r 284
10.7%
n 228
 
8.6%
a 214
 
8.1%
t 201
 
7.6%
i 162
 
6.1%
s 160
 
6.0%
c 134
 
5.1%
o 125
 
4.7%
98
 
3.7%
Other values (15) 639
24.1%

preffed sector
Categorical

Distinct13
Distinct (%)7.0%
Missing1
Missing (%)0.5%
Memory size1.6 KiB
corporate/private sector
63 
it secrtor
49 
government/public sector
41 
undecided
14 
academia/education
Other values (8)
10 

Length

Max length24
Median length24
Mean length18.183784
Min length4

Characters and Unicode

Total characters3364
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)3.8%

Sample

1st rowgovernment/public sector
2nd rowundecided
3rd rowcorporate/private sector
4th rowit secrtor
5th rown.q.

Common Values

ValueCountFrequency (%)
corporate/private sector 63
33.9%
it secrtor 49
26.3%
government/public sector 41
22.0%
undecided 14
 
7.5%
academia/education 8
 
4.3%
others 3
 
1.6%
n.q. 1
 
0.5%
adminstative sector 1
 
0.5%
healthcare sector 1
 
0.5%
judiciary 1
 
0.5%
Other values (3) 3
 
1.6%

Length

2025-09-06T07:51:50.605058image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sector 108
31.5%
corporate/private 63
18.4%
it 49
14.3%
secrtor 49
14.3%
government/public 41
 
12.0%
undecided 14
 
4.1%
academia/education 8
 
2.3%
others 3
 
0.9%
n.q 1
 
0.3%
adminstative 1
 
0.3%
Other values (6) 6
 
1.7%

Most occurring characters

ValueCountFrequency (%)
r 443
13.2%
e 418
12.4%
t 390
11.6%
o 337
10.0%
c 294
8.7%
i 189
 
5.6%
p 168
 
5.0%
a 164
 
4.9%
s 164
 
4.9%
159
 
4.7%
Other values (15) 638
19.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3364
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 443
13.2%
e 418
12.4%
t 390
11.6%
o 337
10.0%
c 294
8.7%
i 189
 
5.6%
p 168
 
5.0%
a 164
 
4.9%
s 164
 
4.9%
159
 
4.7%
Other values (15) 638
19.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3364
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 443
13.2%
e 418
12.4%
t 390
11.6%
o 337
10.0%
c 294
8.7%
i 189
 
5.6%
p 168
 
5.0%
a 164
 
4.9%
s 164
 
4.9%
159
 
4.7%
Other values (15) 638
19.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3364
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 443
13.2%
e 418
12.4%
t 390
11.6%
o 337
10.0%
c 294
8.7%
i 189
 
5.6%
p 168
 
5.0%
a 164
 
4.9%
s 164
 
4.9%
159
 
4.7%
Other values (15) 638
19.0%

Expected_salary
Real number (ℝ)

Missing 

Distinct55
Distinct (%)31.8%
Missing13
Missing (%)7.0%
Infinite0
Infinite (%)0.0%
Mean10.414624
Minimum0.03
Maximum200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2025-09-06T07:51:50.731687image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.03
5-th percentile1
Q14
median6.5
Q311
95-th percentile21.2
Maximum200
Range199.97
Interquartile range (IQR)7

Descriptive statistics

Standard deviation18.263995
Coefficient of variation (CV)1.7536874
Kurtosis69.858605
Mean10.414624
Median Absolute Deviation (MAD)3.5
Skewness7.4507213
Sum1801.73
Variance333.57353
MonotonicityNot monotonic
2025-09-06T07:51:50.876194image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 14
 
7.5%
5 10
 
5.4%
12 10
 
5.4%
6 9
 
4.8%
8 9
 
4.8%
4 9
 
4.8%
15 9
 
4.8%
5.5 8
 
4.3%
9 7
 
3.8%
4.5 6
 
3.2%
Other values (45) 82
44.1%
(Missing) 13
 
7.0%
ValueCountFrequency (%)
0.03 1
 
0.5%
0.23 1
 
0.5%
0.5 4
2.2%
0.75 1
 
0.5%
0.88 1
 
0.5%
1 4
2.2%
1.2 1
 
0.5%
1.5 1
 
0.5%
1.8 2
 
1.1%
2 6
3.2%
ValueCountFrequency (%)
200 1
 
0.5%
80 1
 
0.5%
72 1
 
0.5%
65 1
 
0.5%
60 1
 
0.5%
50 1
 
0.5%
30 1
 
0.5%
24 1
 
0.5%
23 1
 
0.5%
20 4
2.2%

Age
Real number (ℝ)

Zeros 

Distinct26
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.801075
Minimum0
Maximum58
Zeros10
Zeros (%)5.4%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2025-09-06T07:51:51.005452image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.75
Q125
median26
Q330
95-th percentile34.5
Maximum58
Range58
Interquartile range (IQR)5

Descriptive statistics

Standard deviation7.529452
Coefficient of variation (CV)0.29182706
Kurtosis7.3778199
Mean25.801075
Median Absolute Deviation (MAD)2
Skewness-1.4122356
Sum4799
Variance56.692647
MonotonicityNot monotonic
2025-09-06T07:51:51.121919image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
25 46
24.7%
30 30
16.1%
28 14
 
7.5%
24 14
 
7.5%
26 13
 
7.0%
0 10
 
5.4%
27 10
 
5.4%
35 6
 
3.2%
23 5
 
2.7%
22 4
 
2.2%
Other values (16) 34
18.3%
ValueCountFrequency (%)
0 10
 
5.4%
19 1
 
0.5%
21 3
 
1.6%
22 4
 
2.2%
23 5
 
2.7%
23.5 3
 
1.6%
24 14
 
7.5%
24.5 3
 
1.6%
25 46
24.7%
25.5 3
 
1.6%
ValueCountFrequency (%)
58 1
 
0.5%
50 1
 
0.5%
40 2
 
1.1%
35 6
 
3.2%
33 1
 
0.5%
32.5 2
 
1.1%
32 4
 
2.2%
30.5 1
 
0.5%
30 30
16.1%
29.5 1
 
0.5%
Distinct3
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
no
82 
maybe
64 
yes
40 

Length

Max length5
Median length3
Mean length3.2473118
Min length2

Characters and Unicode

Total characters604
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmaybe
2nd rowmaybe
3rd rowmaybe
4th rowyes
5th rowmaybe

Common Values

ValueCountFrequency (%)
no 82
44.1%
maybe 64
34.4%
yes 40
21.5%

Length

2025-09-06T07:51:51.240499image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-06T07:51:51.319335image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 82
44.1%
maybe 64
34.4%
yes 40
21.5%

Most occurring characters

ValueCountFrequency (%)
e 104
17.2%
y 104
17.2%
n 82
13.6%
o 82
13.6%
a 64
10.6%
m 64
10.6%
b 64
10.6%
s 40
 
6.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 604
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 104
17.2%
y 104
17.2%
n 82
13.6%
o 82
13.6%
a 64
10.6%
m 64
10.6%
b 64
10.6%
s 40
 
6.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 604
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 104
17.2%
y 104
17.2%
n 82
13.6%
o 82
13.6%
a 64
10.6%
m 64
10.6%
b 64
10.6%
s 40
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 604
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 104
17.2%
y 104
17.2%
n 82
13.6%
o 82
13.6%
a 64
10.6%
m 64
10.6%
b 64
10.6%
s 40
 
6.6%

Family_income
Categorical

Distinct6
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
3 - 7 lakhs
69 
7 - 10 lakhs
31 
below 3 lakhs
29 
10 - 12 lakhs
23 
15 lakhs above
22 

Length

Max length14
Median length13
Mean length12.951613
Min length12

Characters and Unicode

Total characters2409
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3 - 7 lakhs
2nd row10 - 12 lakhs
3rd row3 - 7 lakhs
4th row3 - 7 lakhs
5th row3 - 7 lakhs

Common Values

ValueCountFrequency (%)
3 - 7 lakhs 69
37.1%
7 - 10 lakhs 31
16.7%
below 3 lakhs 29
15.6%
10 - 12 lakhs 23
 
12.4%
15 lakhs above 22
 
11.8%
12 - 15 lakhs 12
 
6.5%

Length

2025-09-06T07:51:51.444457image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-06T07:51:51.542044image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
lakhs 186
26.8%
135
19.5%
7 100
14.4%
3 98
14.1%
10 54
 
7.8%
12 35
 
5.1%
15 34
 
4.9%
below 29
 
4.2%
above 22
 
3.2%

Most occurring characters

ValueCountFrequency (%)
645
26.8%
l 215
 
8.9%
a 208
 
8.6%
s 186
 
7.7%
h 186
 
7.7%
k 186
 
7.7%
- 135
 
5.6%
1 123
 
5.1%
7 100
 
4.2%
3 98
 
4.1%
Other values (8) 327
13.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2409
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
645
26.8%
l 215
 
8.9%
a 208
 
8.6%
s 186
 
7.7%
h 186
 
7.7%
k 186
 
7.7%
- 135
 
5.6%
1 123
 
5.1%
7 100
 
4.2%
3 98
 
4.1%
Other values (8) 327
13.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2409
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
645
26.8%
l 215
 
8.9%
a 208
 
8.6%
s 186
 
7.7%
h 186
 
7.7%
k 186
 
7.7%
- 135
 
5.6%
1 123
 
5.1%
7 100
 
4.2%
3 98
 
4.1%
Other values (8) 327
13.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2409
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
645
26.8%
l 215
 
8.9%
a 208
 
8.6%
s 186
 
7.7%
h 186
 
7.7%
k 186
 
7.7%
- 135
 
5.6%
1 123
 
5.1%
7 100
 
4.2%
3 98
 
4.1%
Other values (8) 327
13.6%
Distinct12
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
Business
68 
Government Job
45 
Teaching
13 
Private Job
13 
Retired
12 
Other values (7)
35 

Length

Max length14
Median length13
Mean length9.5967742
Min length5

Characters and Unicode

Total characters1785
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUnknown
2nd rowGovernment Job
3rd rowBusiness
4th rowBusiness
5th rowBusiness

Common Values

ValueCountFrequency (%)
Business 68
36.6%
Government Job 45
24.2%
Teaching 13
 
7.0%
Private Job 13
 
7.0%
Retired 12
 
6.5%
Other 11
 
5.9%
Unknown 7
 
3.8%
Legal Sector 5
 
2.7%
Finance 4
 
2.2%
Engineering 4
 
2.2%
Other values (2) 4
 
2.2%

Length

2025-09-06T07:51:51.664438image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
business 68
27.1%
job 58
23.1%
government 45
17.9%
teaching 13
 
5.2%
private 13
 
5.2%
retired 12
 
4.8%
other 11
 
4.4%
unknown 7
 
2.8%
sector 7
 
2.8%
legal 5
 
2.0%
Other values (4) 12
 
4.8%

Most occurring characters

ValueCountFrequency (%)
e 249
13.9%
n 212
11.9%
s 204
11.4%
o 119
 
6.7%
i 118
 
6.6%
r 94
 
5.3%
t 90
 
5.0%
B 68
 
3.8%
u 68
 
3.8%
65
 
3.6%
Other values (23) 498
27.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1785
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 249
13.9%
n 212
11.9%
s 204
11.4%
o 119
 
6.7%
i 118
 
6.6%
r 94
 
5.3%
t 90
 
5.0%
B 68
 
3.8%
u 68
 
3.8%
65
 
3.6%
Other values (23) 498
27.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1785
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 249
13.9%
n 212
11.9%
s 204
11.4%
o 119
 
6.7%
i 118
 
6.6%
r 94
 
5.3%
t 90
 
5.0%
B 68
 
3.8%
u 68
 
3.8%
65
 
3.6%
Other values (23) 498
27.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1785
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 249
13.9%
n 212
11.9%
s 204
11.4%
o 119
 
6.7%
i 118
 
6.6%
r 94
 
5.3%
t 90
 
5.0%
B 68
 
3.8%
u 68
 
3.8%
65
 
3.6%
Other values (23) 498
27.9%

Spending
Real number (ℝ)

Missing 

Distinct33
Distinct (%)21.2%
Missing30
Missing (%)16.1%
Infinite0
Infinite (%)0.0%
Mean7.606359
Minimum0
Maximum100
Zeros1
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2025-09-06T07:51:51.771980image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median5
Q310
95-th percentile20
Maximum100
Range100
Interquartile range (IQR)8

Descriptive statistics

Standard deviation10.173088
Coefficient of variation (CV)1.3374452
Kurtosis45.060846
Mean7.606359
Median Absolute Deviation (MAD)3.5
Skewness5.5543568
Sum1186.592
Variance103.49173
MonotonicityNot monotonic
2025-09-06T07:51:51.891784image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
10 22
11.8%
1 19
10.2%
2 13
 
7.0%
4 12
 
6.5%
5 10
 
5.4%
20 10
 
5.4%
15 8
 
4.3%
3.5 6
 
3.2%
3 6
 
3.2%
6 6
 
3.2%
Other values (23) 44
23.7%
(Missing) 30
16.1%
ValueCountFrequency (%)
0 1
 
0.5%
0.1 1
 
0.5%
0.4 1
 
0.5%
0.5 3
 
1.6%
1 19
10.2%
1.092 1
 
0.5%
1.5 4
 
2.2%
2 13
7.0%
2.5 5
 
2.7%
3 6
 
3.2%
ValueCountFrequency (%)
100 1
 
0.5%
50 1
 
0.5%
30 1
 
0.5%
25 1
 
0.5%
22.5 1
 
0.5%
20 10
5.4%
17.5 1
 
0.5%
15 8
4.3%
13.5 1
 
0.5%
12.5 1
 
0.5%
Distinct5
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
3
48 
5
48 
4
40 
1
37 
2
13 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters186
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row4
3rd row5
4th row4
5th row3

Common Values

ValueCountFrequency (%)
3 48
25.8%
5 48
25.8%
4 40
21.5%
1 37
19.9%
2 13
 
7.0%

Length

2025-09-06T07:51:52.014601image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-06T07:51:52.096531image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3 48
25.8%
5 48
25.8%
4 40
21.5%
1 37
19.9%
2 13
 
7.0%

Most occurring characters

ValueCountFrequency (%)
3 48
25.8%
5 48
25.8%
4 40
21.5%
1 37
19.9%
2 13
 
7.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 186
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 48
25.8%
5 48
25.8%
4 40
21.5%
1 37
19.9%
2 13
 
7.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 186
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 48
25.8%
5 48
25.8%
4 40
21.5%
1 37
19.9%
2 13
 
7.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 186
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 48
25.8%
5 48
25.8%
4 40
21.5%
1 37
19.9%
2 13
 
7.0%

friend_influence
Categorical

Distinct5
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
1
71 
3
37 
2
31 
4
31 
5
16 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters186
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row2
4th row5
5th row1

Common Values

ValueCountFrequency (%)
1 71
38.2%
3 37
19.9%
2 31
16.7%
4 31
16.7%
5 16
 
8.6%

Length

2025-09-06T07:51:52.208773image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-06T07:51:52.331295image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 71
38.2%
3 37
19.9%
2 31
16.7%
4 31
16.7%
5 16
 
8.6%

Most occurring characters

ValueCountFrequency (%)
1 71
38.2%
3 37
19.9%
2 31
16.7%
4 31
16.7%
5 16
 
8.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 186
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 71
38.2%
3 37
19.9%
2 31
16.7%
4 31
16.7%
5 16
 
8.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 186
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 71
38.2%
3 37
19.9%
2 31
16.7%
4 31
16.7%
5 16
 
8.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 186
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 71
38.2%
3 37
19.9%
2 31
16.7%
4 31
16.7%
5 16
 
8.6%

mentor_influence
Categorical

Distinct5
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
1
41 
5
40 
4
38 
2
35 
3
32 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters186
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row3
3rd row4
4th row5
5th row1

Common Values

ValueCountFrequency (%)
1 41
22.0%
5 40
21.5%
4 38
20.4%
2 35
18.8%
3 32
17.2%

Length

2025-09-06T07:51:52.520370image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-06T07:51:52.638117image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 41
22.0%
5 40
21.5%
4 38
20.4%
2 35
18.8%
3 32
17.2%

Most occurring characters

ValueCountFrequency (%)
1 41
22.0%
5 40
21.5%
4 38
20.4%
2 35
18.8%
3 32
17.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 186
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 41
22.0%
5 40
21.5%
4 38
20.4%
2 35
18.8%
3 32
17.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 186
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 41
22.0%
5 40
21.5%
4 38
20.4%
2 35
18.8%
3 32
17.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 186
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 41
22.0%
5 40
21.5%
4 38
20.4%
2 35
18.8%
3 32
17.2%
Distinct5
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
1
54 
3
48 
4
32 
5
29 
2
23 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters186
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row4
3rd row3
4th row5
5th row4

Common Values

ValueCountFrequency (%)
1 54
29.0%
3 48
25.8%
4 32
17.2%
5 29
15.6%
2 23
12.4%

Length

2025-09-06T07:51:52.798480image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-06T07:51:52.911306image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 54
29.0%
3 48
25.8%
4 32
17.2%
5 29
15.6%
2 23
12.4%

Most occurring characters

ValueCountFrequency (%)
1 54
29.0%
3 48
25.8%
4 32
17.2%
5 29
15.6%
2 23
12.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 186
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 54
29.0%
3 48
25.8%
4 32
17.2%
5 29
15.6%
2 23
12.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 186
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 54
29.0%
3 48
25.8%
4 32
17.2%
5 29
15.6%
2 23
12.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 186
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 54
29.0%
3 48
25.8%
4 32
17.2%
5 29
15.6%
2 23
12.4%
Distinct5
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
1
61 
3
39 
2
35 
4
28 
5
23 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters186
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row1
4th row5
5th row3

Common Values

ValueCountFrequency (%)
1 61
32.8%
3 39
21.0%
2 35
18.8%
4 28
15.1%
5 23
 
12.4%

Length

2025-09-06T07:51:53.064050image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-06T07:51:53.184204image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 61
32.8%
3 39
21.0%
2 35
18.8%
4 28
15.1%
5 23
 
12.4%

Most occurring characters

ValueCountFrequency (%)
1 61
32.8%
3 39
21.0%
2 35
18.8%
4 28
15.1%
5 23
 
12.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 186
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 61
32.8%
3 39
21.0%
2 35
18.8%
4 28
15.1%
5 23
 
12.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 186
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 61
32.8%
3 39
21.0%
2 35
18.8%
4 28
15.1%
5 23
 
12.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 186
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 61
32.8%
3 39
21.0%
2 35
18.8%
4 28
15.1%
5 23
 
12.4%

personal_choice
Categorical

Distinct5
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
5
85 
4
47 
3
28 
2
14 
1
12 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters186
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row2
3rd row4
4th row4
5th row5

Common Values

ValueCountFrequency (%)
5 85
45.7%
4 47
25.3%
3 28
 
15.1%
2 14
 
7.5%
1 12
 
6.5%

Length

2025-09-06T07:51:53.343483image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-06T07:51:53.458186image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
5 85
45.7%
4 47
25.3%
3 28
 
15.1%
2 14
 
7.5%
1 12
 
6.5%

Most occurring characters

ValueCountFrequency (%)
5 85
45.7%
4 47
25.3%
3 28
 
15.1%
2 14
 
7.5%
1 12
 
6.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 186
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5 85
45.7%
4 47
25.3%
3 28
 
15.1%
2 14
 
7.5%
1 12
 
6.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 186
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5 85
45.7%
4 47
25.3%
3 28
 
15.1%
2 14
 
7.5%
1 12
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 186
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5 85
45.7%
4 47
25.3%
3 28
 
15.1%
2 14
 
7.5%
1 12
 
6.5%

Interactions

2025-09-06T07:51:44.209917image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-06T07:51:38.587973image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-06T07:51:39.643929image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-06T07:51:40.617691image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-06T07:51:42.005358image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-06T07:51:42.749241image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-06T07:51:43.491822image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-06T07:51:44.320178image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-06T07:51:38.801130image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-06T07:51:39.783738image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-06T07:51:40.771348image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-06T07:51:42.169552image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-06T07:51:42.855334image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-06T07:51:43.594080image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-09-06T07:51:38.950435image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-06T07:51:39.926407image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-06T07:51:40.921703image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-06T07:51:42.285802image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-06T07:51:42.953419image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-06T07:51:43.687492image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-06T07:51:44.522320image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-06T07:51:39.094208image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-06T07:51:40.070291image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-06T07:51:41.069569image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-06T07:51:42.381138image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-06T07:51:43.063414image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-06T07:51:43.788347image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-06T07:51:44.624907image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-06T07:51:39.227272image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-06T07:51:40.212070image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-06T07:51:41.206483image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-06T07:51:42.471341image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-06T07:51:43.174283image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-06T07:51:43.882016image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-06T07:51:44.732632image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-06T07:51:39.375792image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-06T07:51:40.359818image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-06T07:51:41.691301image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-06T07:51:42.567769image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-06T07:51:43.292366image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-06T07:51:43.999779image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-06T07:51:44.835022image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-06T07:51:39.508169image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-06T07:51:40.492682image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-06T07:51:41.844266image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-06T07:51:42.657440image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-06T07:51:43.395284image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-06T07:51:44.100828image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-09-06T07:51:53.648524image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
AgeAge GroupCGPADegreeExpected_salaryFamily_incomeFavorite_Subject_10Favorite_Subject_12Financially constrainedGenderMarks_10Marks_12Parent's occupationPercent_10Percent_12SpendingSubjectdf_indexfriend_influencementor_influenceparents_influencepersonal_choicepreffed sectorsocial_expectationssocial_media_influence
Age1.0000.0000.0810.000-0.1040.0000.2820.0000.0000.1400.1730.0630.1930.0000.0290.2110.4510.2400.0000.0000.0000.1570.0000.0000.048
Age Group0.0001.0000.2040.4520.0000.1090.0000.0000.0620.0910.0270.1640.1440.0000.1000.0880.2120.0000.1800.0000.1620.0190.0000.0860.000
CGPA0.0810.2041.0000.1650.0470.0400.0000.0000.0000.0000.3160.3870.0000.2310.252-0.0150.0000.0990.0000.0260.0000.0000.0760.0930.081
Degree0.0000.4520.1651.0000.0000.0000.0000.4150.0280.1210.2780.1520.0890.2420.4130.1830.6970.1310.0930.1580.0000.2170.1050.1160.141
Expected_salary-0.1040.0000.0470.0001.0000.0000.0000.0000.0750.0000.2980.1480.0000.0000.0000.0810.0000.1830.0000.1350.1290.0970.0000.0000.000
Family_income0.0000.1090.0400.0000.0001.0000.0000.1360.2140.0000.0850.0000.0620.1650.0000.1530.1060.0000.0000.1530.1510.0000.0960.0000.098
Favorite_Subject_100.2820.0000.0000.0000.0000.0001.0000.3680.0000.0000.4160.0000.1710.2560.0000.3970.2730.1100.0000.0000.1200.0460.0000.0810.132
Favorite_Subject_120.0000.0000.0000.4150.0000.1360.3681.0000.0000.2600.5070.0000.3060.3240.0000.0730.2950.1640.0000.0000.0000.0260.3660.0000.000
Financially constrained0.0000.0620.0000.0280.0750.2140.0000.0001.0000.0250.1320.1860.0000.0000.1010.0000.1480.0960.1470.0860.0740.1850.1380.0000.000
Gender0.1400.0910.0000.1210.0000.0000.0000.2600.0251.0000.0380.0000.0000.0000.0540.0000.0000.6830.0000.0000.0850.0000.0000.0000.028
Marks_100.1730.0270.3160.2780.2980.0850.4160.5070.1320.0381.0000.3890.1410.4660.3020.1730.0000.1950.0000.0000.0000.0140.2440.0580.000
Marks_120.0630.1640.3870.1520.1480.0000.0000.0000.1860.0000.3891.0000.0000.2420.5450.0610.0000.0820.0890.0000.0000.0000.0650.0000.072
Parent's occupation0.1930.1440.0000.0890.0000.0620.1710.3060.0000.0000.1410.0001.0000.0000.0000.0000.0000.0660.0520.0960.0860.0000.2830.0280.101
Percent_100.0000.0000.2310.2420.0000.1650.2560.3240.0000.0000.4660.2420.0001.0000.2900.0000.2420.0500.0000.0680.0450.0000.0000.0000.000
Percent_120.0290.1000.2520.4130.0000.0000.0000.0000.1010.0540.3020.5450.0000.2901.0000.0000.2540.1610.0640.0910.0000.0000.0000.0360.000
Spending0.2110.088-0.0150.1830.0810.1530.3970.0730.0000.0000.1730.0610.0000.0000.0001.0000.4880.1800.0000.0580.1320.0000.0000.0490.000
Subject0.4510.2120.0000.6970.0000.1060.2730.2950.1480.0000.0000.0000.0000.2420.2540.4881.0000.1580.0000.0410.0000.2070.1080.0000.000
df_index0.2400.0000.0990.1310.1830.0000.1100.1640.0960.6830.1950.0820.0660.0500.1610.1800.1581.0000.0870.0390.0000.0000.0000.0610.054
friend_influence0.0000.1800.0000.0930.0000.0000.0000.0000.1470.0000.0000.0890.0520.0000.0640.0000.0000.0871.0000.2610.2150.1150.1770.1270.228
mentor_influence0.0000.0000.0260.1580.1350.1530.0000.0000.0860.0000.0000.0000.0960.0680.0910.0580.0410.0390.2611.0000.2420.1240.0000.0620.131
parents_influence0.0000.1620.0000.0000.1290.1510.1200.0000.0740.0850.0000.0000.0860.0450.0000.1320.0000.0000.2150.2421.0000.1680.0750.1360.131
personal_choice0.1570.0190.0000.2170.0970.0000.0460.0260.1850.0000.0140.0000.0000.0000.0000.0000.2070.0000.1150.1240.1681.0000.1440.1270.173
preffed sector0.0000.0000.0760.1050.0000.0960.0000.3660.1380.0000.2440.0650.2830.0000.0000.0000.1080.0000.1770.0000.0750.1441.0000.0940.121
social_expectations0.0000.0860.0930.1160.0000.0000.0810.0000.0000.0000.0580.0000.0280.0000.0360.0490.0000.0610.1270.0620.1360.1270.0941.0000.260
social_media_influence0.0480.0000.0810.1410.0000.0980.1320.0000.0000.0280.0000.0720.1010.0000.0000.0000.0000.0540.2280.1310.1310.1730.1210.2601.000

Missing values

2025-09-06T07:51:45.038730image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-09-06T07:51:45.319991image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-09-06T07:51:45.641286image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

df_indexAge GroupGenderCollege NameDegreeSubjectPercent_10Favorite_Subject_10Marks_10Percent_12Favorite_Subject_12Marks_12CGPAco-curricular activityprofessionpreffed sectorExpected_salaryAgeFinancially constrainedFamily_incomeParent's occupationSpendingparents_influencefriend_influencementor_influencesocial_expectationssocial_media_influencepersonal_choice
0018 - 20finstitute of engineering and managementbbaanalytics85.0biology97.075.0biology99.08.0nodata analystNaNNaN0.0maybe3 - 7 lakhsUnknownNaN311115
1120 - 22finstitute of engineering and managementbbafinance85.0mathematics99.075.0economics80.09.0content writingfinancial servicesgovernment/public sector3.528.0maybe10 - 12 lakhsGovernment Job10.0413422
2218 - 20fasutosh collegeb.sc / bsstatistics85.0mathematics98.085.0mathematics86.06.0dancechartered accountantundecided8.025.0maybe3 - 7 lakhsBusiness2.5524314
3318 - 20fsister nivedita universitybapolitical science95.0socialscience94.085.0politicalscience87.07.0not right nowentrepreneurshipcorporate/private sector2.00.0yes3 - 7 lakhsBusinessNaN455554
4420 - 22fsister nivedita universitymsc / mspsychology65.0maths60.065.0accounts65.07.0nochartered accountantit secrtor0.527.0maybe3 - 7 lakhsBusiness10.0311435
5520 - 22fsister nivedita universitymaenglish75.0english81.075.0english87.06.0dancechartered accountantn.q.6.028.0no3 - 7 lakhsFinance1.0555445
6620 - 22fsister nivedita universitymaenglish55.0english82.065.0english72.08.0noprofessoracademia/education3.528.5maybebelow 3 lakhsBusinessNaN111515
7720 - 22fsister nivedita universitymaenglish75.0english82.085.0english88.06.0noprofessorit secrtor13.025.0nobelow 3 lakhsBusiness2.0512324
8820 - 22fsister nivedita universitybaenglish85.0science80.075.0english80.08.0noprofessorcorporate/private sector3.525.0yes3 - 7 lakhsOther2.0512523
9922 - 25funiversity of calcuttam com.accountancy85.0science87.085.0accountancy92.08.0karatecommercial undecidedsgovernment/public sector8.50.0no3 - 7 lakhsBusinessNaN213115
df_indexAge GroupGenderCollege NameDegreeSubjectPercent_10Favorite_Subject_10Marks_10Percent_12Favorite_Subject_12Marks_12CGPAco-curricular activityprofessionpreffed sectorExpected_salaryAgeFinancially constrainedFamily_incomeParent's occupationSpendingparents_influencefriend_influencementor_influencesocial_expectationssocial_media_influencepersonal_choice
17618220 - 22mtechno india universityb tech.computer science engineering95.0mathematics98.095.0NaNNaN8.0dance, acting, footballactorcreative fieldsNaN0.0yes7 - 10 lakhsGovernment Job6.0552551
17718318 - 20masutosh collegebamass com55.0english91.075.0bstd82.06.0football, anchoringentrepreneurshipgovernment/public sector11.524.0maybe7 - 10 lakhsGovernment JobNaN131544
17818420 - 22minstitute of engineering and managementbbamanagement75.0maths96.085.0computerscience97.09.0graphics designerentrepreneurshipundecided4.030.0yes10 - 12 lakhsOther20.0332124
17918520 - 22mipgmerbptphysiotherapy95.0science97.095.0physics85.09.0sportsentrepreneurshipgovernment/public sector20.028.0no3 - 7 lakhsTeaching20.0515115
18018618 - 20mst xaviers universityllbNaN75.0politicalscience82.085.0economics88.07.0nolitigationgovernment/public sector6.530.0no15 lakhs aboveLegal Sector10.0523544
18118718 - 20minstitute of engineering and managementb tech.computer science engineering95.0sst95.075.0english92.00.0noinvestment bankerundecided6.540.0maybe3 - 7 lakhsOther10.0511313
18218818 - 20muniversity of engineering and managementb tech.computer science engineering85.0mathematics97.075.0biology85.00.0sportartificial intelligenceit secrtor10.024.0maybebelow 3 lakhsBusiness6.0445534
18318920 - 22mvivekananda college thakurpukurb.sc / bsgeography75.0geography80.095.0geography99.07.0NaNgis analystgovernment/public sector2.528.0nobelow 3 lakhsRetired7.5112115
18419018 - 20mst thomas college of engineering and technologyb tech.information technology95.0mathematics100.085.0science97.08.0NaNsoftware development engineerit secrtor8.535.0no15 lakhs abovePrivate Job4.5421345
18519118 - 20ojadavpur universitybaenglish95.0biology100.095.0psychology98.06.0music/writingundecidedgovernment/public sector4.027.0maybe3 - 7 lakhsGovernment Job1.0324345